Setting aside other psychological aspects,
we focus here on the core computational question of
whether a neural network can be endowed with the ability
to learn about structure in a way that allows it to
generalise to instantiations of that structure constructed
from novel elements.
To this end, we frame the problem as
a supervised learning task in which each of the three
words in the input sequence are represented
by a one-hot vector, thus ensuring there is
no featural overlap between train and test,
and the output consists of a pair of units
representing the categories ABA and ABB.
The data used in our experiments are taken from
the [1]} paper, and we evaluate the models
in terms of their ability to generalise to test inputs
that are constructed from words not seen during training.
As a result of the localist coding of words we have employed
the training and test inputs share no active units in common,
and so successful generalisation requires that the network
is able to generalise outside its training space.
